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ABSTRACT We use a simulation-based modelling approach to analyse the anisotropic clustering of the BOSS LOWZ sample over the radial range $$0.4 \, h^{-1} \, \mathrm{Mpc}$$ to $$63 \, h^{-1} \, \mathrm{Mpc}$$, significantly extending what is possible with a purely analytic modelling framework. Our full-scale analysis yields constraints on the growth of structure that are a factor of two more stringent than any other study on large scales at similar redshifts. We infer fσ8 = 0.471 ± 0.024 at $$z$$ ≈ 0.25, and fσ8 = 0.430 ± 0.025 at $$z$$ ≈ 0.40; the corresponding ΛCDM predictions of the Planck cosmic microwave background (CMB) analysis are 0.470 ± 0.006 and 0.476 ± 0.005, respectively. Our results are thus consistent with Planck, but also follow the trend seen in previous low-redshift measurements of fσ8 falling slightly below the ΛCDM + CMB prediction. We find that small- and large-radial scales yield mutually consistent values of fσ8, but there are 1−2.5σ hints of small scales ($$\lt 10 \, h^{-1} \, \mathrm{Mpc}$$) preferring lower values for fσ8 relative to larger scales. We analyse the constraining power of the full range of radial scales, finding that most of the multipole information about fσ8 is contained in the scales $$2 \, h^{-1} \, \mathrm{Mpc}\lesssim s \lesssim 20 \, h^{-1} \, \mathrm{Mpc}$$. Evidently, once the cosmological information of the quasi-to-nonlinear regime has been harvested, large-scale modes contain only modest additional information about structure growth. Finally, we compare predictions for the galaxy–galaxy lensing amplitude of the two samples against measurements from SDSS and assess the lensing-is-low effect in light of our findings. 

ABSTRACT The canonical Lambda cold dark matter (ΛCDM) cosmological model makes precise predictions for the clustering and lensing properties of galaxies. It has been shown that the lensing amplitude of galaxies in the Baryon Oscillation Spectroscopic Survey (BOSS) is lower than expected given their clustering properties. We present new measurements and modelling of galaxies in the BOSS LOWZ sample. We focus on the radial and stellar mass dependence of the lensing amplitude mismatch. We find an amplitude mismatch of around $$35{{\ \rm per\ cent}}$$ when assuming ΛCDM with Planck Cosmological Microwave Background (CMB) constraints. This offset is independent of halo mass and radial scale in the range Mhalo ∼ 1013.3−1013.9h−1 M⊙ and $$r=0.1\!-\!60 \, h^{-1} \mathrm{Mpc}$$ ($$k \approx 0.05\!-\!20 \, h \, {\rm Mpc}^{-1}$$). The observation that the offset is both mass and scale independent places important constraints on the degree to which astrophysical processes (baryonic effects, assembly bias) can fully explain the effect. This scale independence also suggests that the ‘lensing is low’ effect on small and large radial scales probably have the same physical origin. Resolutions based on new physics require a nearly uniform suppression, relative to ΛCDM predictions, of the amplitude of matter fluctuations on these scales. The possible causes of this are tightly constrained by measurements of the CMB and of the low-redshift expansion history. 

Abstract Many measurements at the LHC require efficient identification of heavy-flavour jets, i.e. jets originating from bottom (b) or charm (c) quarks. An overview of the algorithms used to identify c jets is described and a novel method to calibrate them is presented. This new method adjusts the entire distributions of the outputs obtained when the algorithms are applied to jets of different flavours. It is based on an iterative approach exploiting three distinct control regions that are enriched with either b jets, c jets, or light-flavour and gluon jets. Results are presented in the form of correction factors evaluated using proton-proton collision data with an integrated luminosity of 41.5 fb -1 at  √s = 13 TeV, collected by the CMS experiment in 2017. The closure of the method is tested by applying the measured correction factors on simulated data sets and checking the agreement between the adjusted simulation and collision data. Furthermore, a validation is performed by testing the method on pseudodata, which emulate various mismodelling conditions. The calibrated results enable the use of the full distributions of heavy-flavour identification algorithm outputs, e.g. as inputs to machine-learning models. Thus, they are expected to increase the sensitivity of future physics analyses.